It is well known that drug response varies greatly among different people with respect to efficacy and side effects experienced. For example, aspirin causes gastrointestinal distress in some users; certain antihistamine drugs are not beneficial for all. This population variability can also be seen in the treatment of serious and lethal diseases such as cancer. A patient will begin a treatment, and, depending on the efficacy and side effects, the clinician will decide if he/she should continue the treatment or switch to another regimen.
When treating certain serious diseases, this try-and-switch method can have severe consequences; this is particularly true in the time-sensitive case of cancer treatment. One does not know the efficacy of different chemotherapies for an individual, thus making it difficult to design an effective treatment plan; the outcomes of their treatments are essentially random. As most cancer patients will receive chemotherapy during the course of their disease, many will suffer from the treatment's ineffectiveness and experience possible side effects to their already fragile health.
This situation is a result of the limited scope in current clinical trial design. These trials are designed to determine drug efficacy across a patient population. The results of the trials represent the statistical probability of effectiveness for a group of patients. There is no specific information about the drug's efficacy for an individual patient.
Conventional In Vitro and In Vivo Drug Efficacy Assays
For many years, drug efficacy prediction has been determined using in vitro and in vivo assays, which are designed to measure the response of tumors to drugs in a simulated environment. Although many assays are highly sophisticated with careful experimental design and excellent technique, there are basic limitations to these approaches. Furthermore, most clinicians question the validity of these experiments.
Generally, in vitro chemosensitivity assays are used for predicting patient response to drug therapy. Primary cancerous or metastatic cells are isolated from tumors and incubated with chemotherapeutic drugs. Cell survival is then assessed, and results are interpreted to determine a patient's tumor sensitivity or resistance to the drugs. There are many problems with these experiments. First, there are issues independent of the assay used, which include the differences between primary versus metastatic cells, the choice of drug concentrations, and heterogeneity in tumor specimens. Secondly, there are assay-specific issues such as the inability to distinguish between the growth of malignant and nonmalignant cells in culture. Thirdly, there are technical difficulties with the human tumor cloning assay, where about half of the specimens do not experience growth; as a result, colonies are unavailable for quantification in a drug response readout. In addition to the above deficiencies, these experiments may have other issues; for example, the requirement of long incubation time (14 to 28 days) for the assay is impractical for use in clinical practice. Furthermore, there are significant differences between in vitro assay conditions and in vivo physiological environments, thereby rendering doubts in the validity of the drug response observed in vitro and its potential clinical application.
One alternative to the in vitro experiments has been the extreme drug resistance study (EDR), which focuses on drug resistance rather than drug sensitivity. In these experiments, the tumors are treated with very high drug concentrations for a long exposure time. The hypothesis is that if the tumors fail to exhibit response under these extreme conditions the patient will also be unresponsive to the drug. Kern and Weisenthal first reported positive EDR results (See, Kern, D. H., Weisenthal, L. M., “Highly specific prediction of antineoplastic drug resistance with an in vitro assay using suprapharmacologic drug exposures,” (1990) J Natl Cancer Inst; 7:582), incorporated herein by reference in its entirety, but they could not be confirmed by others in subsequent studies. In a study of patients with stage II ovarian cancer, no difference in 3-year survival rate was predicted by the EDR assay. (See, Orr, J. W. Jr, Orr, P, Kern, D. H., Cost-effective treatment of women with advanced ovarian cancer by cytoreductive surgery and chemotherapy directed by an in vitro assay for drug resistance, (1999) Cancer J Sci Am 5:174-178, incorporated herein by reference in its entirety). In another study by Eltabbakh (See, Eltabbakh, G. H., Piver, M. S., Hempling, R. E., et al. “Correlation between extreme drug resistance assay and response to primary paclitaxel and cisplatin in patients with epithelial ovarian cancer,” (1998) Gynecol Oncol; 70:392-397, incorporated herein by reference in its entirety), on 75 ovarian cancer patients, the EDR assay also failed to demonstrate any advantage in predicting drug response. A prospective study on 95 colorectal and appendiceal cancer patients also failed to correlate sensitivity or resistance of tumors with the in vitro prediction assay. (See, “Fernandez-Trigo, V., Shansa, F., Vidal-Jove, J., et al. Prognostic implications of chemoresistance-sensitivity assays for colorectal and apendiceal cancer,” (1995) Am J Clin Oncol; 18:454-460, incorporated herein by reference in its entirety).
An improvement to the in vitro approach is the in vivo technique, which studies the three-dimensional cell structure of tumors, as well as the metabolic, activational effects of the drug. In vivo experiments typically utilize immune-deficient mice by implanting tumor cells under the subrenal capsule or inoculating the animals with cancer cells. Some promising results were reported for the former. (See, for example, Bogden, A. E., “The subrenal capsule assay and its predictive value in oncology,” (1985) Ann Chir Gynaecol; 74 (suppl 199):12), incorporated herein by reference in its entirety). While in vivo experimentation is designed to closely mimic the complexities of the biological system, there are still many factors that cannot be duplicated through animal modeling. For example, the mouse's drug metabolism and host toxicity may not be comparable to that in the human, or the tumors introduced could behave differently than the tumors in the human system. (See Cunningham, D. et al, “The 6-day subrenal capsule assay is of no value with primary surgical explants from gastric cancer,” (1986) Br. J Cancer; 54:519, incorporated herein by reference in its entirety). In addition, the significant duration of time needed in these experiments makes it difficult to use these assays for diagnostic purposes. Often, an in vivo experiment can take up to three months before results are obtained, which is acceptable in cancer research, but highly unlikely to be applicable in clinical practice.
In summary, both in vivo and in vitro experimental techniques for predicting individual patient drug response have inherent problems. Most obvious are the differences imposed by the experimental system, which are in contrast to the physiological environment in a patient. Also, disparity in tumor types, levels of drug concentration, and issues of quality control can prove to be problematic in these in vitro predictive tests.
Recent Status of Pharmacogenetics
Because many diseases are genetic disorders, gene expression is expected to be able to predict their response to treatment. There are a few cases of a single marker gene being linked to chemotherapeutic efficacy. For example, in treatment of breast cancer, Tamoxifen is used in ER-positive tumors, and Herceptin is used when the growth factor receptor HER2 is overexpressed. However, these are exceptions; generally, one cannot expect a single marker gene to reliably predict a drug's effectiveness. Instead, many genes related to drug response need to be identified. Subsequent development of an optimal method for combining the information from these genes is required. This new approach affects a long list of diseases related to genetic disorder as reported by NIH: Cancer, Blood and Lymph Diseases, The Digestive System, Ear Nose and Throat, Diseases of the Eye, Female-Specific Diseases, Glands and Hormones, The Heart and Blood Vessels, Diseases of the Immune System, Male-Specific Diseases, Muscle and Bone, Neonatal Diseases, The Nervous System, Nutritional and Metabolic Diseases, Respiratory Diseases, Skin and Connective Tissue. (See,“Genes and Disease”, by National Center for Biotechnology Information,” incorporated herein by reference in its entirety.) The goal of pharmacogenetics is to understand the genetic disorder; the results would provide a better predictive model than that of a single marker gene.
The recent advent of DNA microarray, or gene chip technology, provides a platform for potentially analyzing all human genes in a single experiment. This technique has revolutionized pharmacological investigations (See, Lander, E. S. et al., “Initial sequencing and analysis of the human genome. Nature 409:860-921, (2001); and Venter, J. C. et al., “The sequence of the human genome. Science,” 291:1304-1351, (2001); incorporated herein by reference in their entireties.) Monitoring gene expression profiles can provide insight into the molecular fingerprint of diseases. This technique also provides a basis for studying therapeutic treatments, environmental agents, and can ultimately help in distinguishing between responders and non-responders to a given drug, as well as predicting toxicity and other adverse effects on the basis of altered patterns in expression profiles.
As of yet, clinical gene-based cancer research is very limited. Some have used microarrays to infer differences between normal and cancerous tissues (See, Welsh, J. B., et al., “Analysis of Gene Expression Profiles in Normal and Neoplastic Ovarian Tissue Samples Identifies Candidate Molecular Markers of Epithelial Ovarian Cancer,” Proc. Natl. Acad. Sci. USA 98, 1176-1181, (2001); and Alon, U., et al., “Broad Patterns of Gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays,” Proc. Natl. Acad. Sci. USA, 96, 6745-6750, (1999); incorprated herein by reference in their entireties.) These studies were designed to find marker genes or co-regulated genes. Another application for microarray study has been the molecular classification of cancer tissues using their pathological characteristics (e.g. metastatic, invasive, or AML vs. ALL in leukemia). These studies have been successful in separating breast, melanoma, leukemia, lung, and lymphoma tissues according to their genetic profiles (See Laura J. van't Veer, Hongyue Dai, et al., “Gene expression profiling predicts clinical outcome of breast cancer, Nature, 415, 530-536 (2002); Marc J. van de Vijver, et al., “A gene expression signature as a predictor of survival in breast cancer,” N Engl J Med, 347, No. 25, 1999-2009 (2002); Bittner, M., et al., “Molecular classification of cutaneous malignant melanoma by gene expression profiling,” Nature 406, 536-540 (2000); Golub, T. R., et al., “Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring,” Science, 286, 531-537 (1999); Bhattacharjee, A., et al., “Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses,” Proc. Natl. Acad. Sci. USA 98, 13790-13795 (2001); and Alizadeh, Ash A., et al., “Distinct Types of Diffuse Large B-Cell Lymphoma Identified by Gene Expression Profiling,” Nature, 403, 503-511 (2000); incorporated herein by reference in their entireties.)
However, none of these studies address the predictability of treatment outcome.
Current Research Status on Predicting Treatment Outcome
Drug resistance in cancer treatment is an especially critical problem. Genomics approaches, such as the use of DNA microarrays, have been used to identify genetic pathways that contribute to drug resistance in cancer. As this approach requires detailed analysis of many individual pathways, it is still far from gaining a comprehensive understanding of the complex relationships among genomic, molecular, cellular, and clinical phenotypes.
Because there is very limited knowledge of genetic pathways in clinical application, DNA microarray analysis has been utilized for studying drug response in in vitro and in vivo environments. Studies have been reported relating genome-wide gene expression from changes in response to chemotherapeutic agents to tumor tissue drug response directly without having to specify the pathway of analysis. (See Staunton, J. E., et al., “Chemosensitivity prediction by transcriptional profiling,” Proc. Natl. Acad. Sci. USA 98, 10787-10792 (2001); Zembutsu, H., et al., “Genome-wide cDNA microarray screening to correlate gene expression profiles with sensitivity of 85 human cancer xenografts to anticancer drugs,” Cancer Res. 62:518-527 (2002); incorporated herein by reference in their entireties). Due to the limitations of the experimental design, these results were not definitive enough to determine if a sample's gene profile could be accurately related to a specific drug response. However, even if further in vitro or in vivo studies had provided positive results, direct clinical application still would not have been possible.
What are needed are methods, systems, and computer program products that predict an individual's treatment outcome from a sampling of a group of patients' biological profiles.